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1.  Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies 
Genome Medicine  2012;4(4):33.
Metabolomics, the non-targeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. Current tissue extraction protocols involve sample destruction, precluding additional uses of the tissue. This is particularly problematic for high value samples with limited availability, such as clinical tumor biopsies that require structural preservation to histologically diagnose and gauge cancer aggressiveness. To overcome this limitation and increase the amount of information obtained from patient biopsies, we developed and characterized a workflow to perform metabolomic analysis and histological evaluation on the same biopsy sample.
Biopsies of ten human tissues (muscle, adrenal gland, colon, lung, pancreas, small intestine, spleen, stomach, prostate, kidney) were placed directly in a methanol solution to recover metabolites, precipitate proteins, and fix tissue. Following incubation, biopsies were removed from the solution and processed for histology. Kidney and prostate cancer tumor and benign biopsies were stained with hemotoxylin and eosin and prostate biopsies were subjected to PIN-4 immunohistochemistry. The methanolic extracts were analyzed for metabolites on GC/MS and LC/MS platforms. Raw mass spectrometry data files were automatically extracted using an informatics system that includes peak identification and metabolite identification software.
Metabolites across all major biochemical classes (amino acids, peptides, carbohydrates, lipids, nucleotides, cofactors, xenobiotics) were measured. The number (ranging from 260 in prostate to 340 in colon) and identity of metabolites were comparable to results obtained with the current method requiring 30 mg ground tissue. Comparing relative levels of metabolites, cancer tumor from benign kidney and prostate biopsies could be distinguished. Successful histopathological analysis of biopsies by chemical staining (hematoxylin, eosin) and antibody binding (PIN-4, in prostate) showed cellular architecture and immunoreactivity were retained.
Concurrent metabolite extraction and histological analysis of intact biopsies is amenable to the clinical workflow. Methanol fixation effectively preserves a wide range of tissues and is compatible with chemical staining and immunohistochemistry. The method offers an opportunity to augment histopathological diagnosis and tumor classification with quantitative measures of biochemicals in the same tissue sample. Since certain biochemicals have been shown to correlate with disease aggressiveness, this method should prove valuable as an adjunct to differentiate cancer aggressiveness.
PMCID: PMC3446261  PMID: 22546470
2.  An improved method for constructing tissue microarrays from prostate needle biopsy specimens 
Journal of Clinical Pathology  2009;62(8):694-698.
Prostate cancer diagnosis is routinely made by the histopathological examination of formalin fixed needle biopsy specimens. Frequently this is the only cancer tissue available from the patient for the analysis of diagnostic and prognostic biomarkers. There is, therefore, an urgent need for methods that allow the high-throughput analysis of these biopsy samples using immunohistochemical (IHC) markers and fluorescence in situ hybridisation (FISH) analysis based markers.
A method that allows the construction of tissue microarrays (TMAs) from diagnostic prostate needle biopsy cores has previously been reported. However, the technique only allows the production of low-density biopsy TMAs with a maximum of 20 cores per TMA. Here two methods are presented that allow the rapid and uniform production of biopsy TMAs containing between 54 and 72 biopsy cores. IHC and FISH techniques were used to detect biomarker status.
Biopsy TMAs were constructed from prostate needle biopsy specimens taken from 102 patients entered into an active surveillance trial and 201 patients in a radiotherapy trial. The detection rate for cancer in slices of these biopsy TMAs was 66% and 79% respectively. Slices of a biopsy TMA prepared from biopsies from active surveillance patients were used to detect multiple IHC markers and to score TMPRSS2-ERG fusion status in a FISH-based assay.
The construction of biopsy TMAs provides an effective method for the multiplex analysis of IHC and FISH markers and for their assessment as prognostic biomarkers in the context of clinical trials.
PMCID: PMC2709943  PMID: 19638540
3.  Breast Cancer Diagnosis Using a Microfluidic Multiplexed Immunohistochemistry Platform 
PLoS ONE  2010;5(5):e10441.
Biomarkers play a key role in risk assessment, assessing treatment response, and detecting recurrence and the investigation of multiple biomarkers may also prove useful in accurate prediction and prognosis of cancers. Immunohistochemistry (IHC) has been a major diagnostic tool to identify therapeutic biomarkers and to subclassify breast cancer patients. However, there is no suitable IHC platform for multiplex assay toward personalized cancer therapy. Here, we report a microfluidics-based multiplexed IHC (MMIHC) platform that significantly improves IHC performance in reduction of time and tissue consumption, quantification, consistency, sensitivity, specificity and cost-effectiveness.
Methodology/Principal Findings
By creating a simple and robust interface between the device and human breast tissue samples, we not only applied conventional thin-section tissues into on-chip without any additional modification process, but also attained perfect fluid control for various solutions, without any leakage, bubble formation, or cross-contamination. Four biomarkers, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR) and Ki-67, were examined simultaneously on breast cancer cells and human breast cancer tissues. The MMIHC method improved immunoreaction, reducing time and reagent consumption. Moreover, it showed the availability of semi-quantitative analysis by comparing Western blot. Concordance study proved strong consensus between conventional whole-section analysis and MMIHC (n = 105, lowest Kendall's coefficient of concordance, 0.90). To demonstrate the suitability of MMIHC for scarce samples, it was also applied successfully to tissues from needle biopsies.
The microfluidic system, for the first time, was successfully applied to human clinical tissue samples and histopathological diagnosis was realized for breast cancers. Our results showing substantial agreement indicate that several cancer-related proteins can be simultaneously investigated on a single tumor section, giving clear advantages and technical advances over standard immunohistochemical method. This novel concept will enable histopathological diagnosis using numerous specific biomarkers at a time even for small-sized specimens, thus facilitating the individualization of cancer therapy.
PMCID: PMC2862720  PMID: 20454672
4.  Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies 
PLoS Medicine  2008;5(12):e232.
Although it has long been appreciated that ovarian carcinoma subtypes (serous, clear cell, endometrioid, and mucinous) are associated with different natural histories, most ovarian carcinoma biomarker studies and current treatment protocols for women with this disease are not subtype specific. With the emergence of high-throughput molecular techniques, distinct pathogenetic pathways have been identified in these subtypes. We examined variation in biomarker expression rates between subtypes, and how this influences correlations between biomarker expression and stage at diagnosis or prognosis.
Methods and Findings
In this retrospective study we assessed the protein expression of 21 candidate tissue-based biomarkers (CA125, CRABP-II, EpCam, ER, F-Spondin, HE4, IGF2, K-Cadherin, Ki-67, KISS1, Matriptase, Mesothelin, MIF, MMP7, p21, p53, PAX8, PR, SLPI, TROP2, WT1) in a population-based cohort of 500 ovarian carcinomas that was collected over the period from 1984 to 2000. The expression of 20 of the 21 biomarkers differs significantly between subtypes, but does not vary across stage within each subtype. Survival analyses show that nine of the 21 biomarkers are prognostic indicators in the entire cohort but when analyzed by subtype only three remain prognostic indicators in the high-grade serous and none in the clear cell subtype. For example, tumor proliferation, as assessed by Ki-67 staining, varies markedly between different subtypes and is an unfavourable prognostic marker in the entire cohort (risk ratio [RR] 1.7, 95% confidence interval [CI] 1.2%–2.4%) but is not of prognostic significance within any subtype. Prognostic associations can even show an inverse correlation within the entire cohort, when compared to a specific subtype. For example, WT1 is more frequently expressed in high-grade serous carcinomas, an aggressive subtype, and is an unfavourable prognostic marker within the entire cohort of ovarian carcinomas (RR 1.7, 95% CI 1.2%–2.3%), but is a favourable prognostic marker within the high-grade serous subtype (RR 0.5, 95% CI 0.3%–0.8%).
The association of biomarker expression with survival varies substantially between subtypes, and can easily be overlooked in whole cohort analyses. To avoid this effect, each subtype within a cohort should be analyzed discretely. Ovarian carcinoma subtypes are different diseases, and these differences should be reflected in clinical research study design and ultimately in the management of ovarian carcinoma.
David Huntsman and colleagues describe the associations between biomarker expression patterns and survival in different ovarian cancer subtypes. They suggest that the management of ovarian cancer should reflect differences between these subtypes.
Editors' Summary
Every year, about 200,000 women develop ovarian cancer and more than 100,000 die from the disease. Ovarian epithelial cancer (carcinoma) occurs when epithelial cells from the ovary or fallopian tube acquire mutations or equivalent changes that allow them to grow uncontrollably within one of the ovaries (two small organs in the pelvis that produce eggs) and acquire the potential to spread around the body (metastasize). While the cancer is confined to the ovaries, cancer specialists call this stage I disease; 70%–80% of women diagnosed with stage I ovarian cancer survive for at least 5 y. However, only a fifth of ovarian cancers are diagnosed at this stage; in the majority of patients the cancer has spread into the pelvis (stage II disease), into the peritoneal cavity (the space around the gut, stomach, and liver; stage III disease), or metastasized to distant organs such as brain (stage IV disease). This peritoneal spread might be associated with often only vague abdominal pain and mild digestive disturbances. Patients with advanced-stage ovarian carcinoma are treated with a combination of surgery and chemotherapy but, despite recent advances in treatment, only 15% of women diagnosed with stage IV disease survive for 5 y.
Why Was This Study Done?
Although it is usually regarded as a single disease, there are actually several distinct subtypes of ovarian carcinoma. These are classified according to their microscopic appearance as high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous ovarian carcinomas. These subtypes develop differently and respond differently to chemotherapy. Yet scientists studying ovarian carcinoma usually regard this cancer as a single entity, and current treatment protocols for the disease are not subtype specific. Might better progress be made toward understanding ovarian carcinoma and toward improving its treatment if each subtype were treated as a separate disease? Why are some tumors confined to the ovary, whereas the majority spread beyond the ovary at time of diagnosis? In this study, the researchers address these questions by asking whether correlations between the expression of “biomarkers” (molecules made by cancer cells that can be used to detect tumors and to monitor treatment effectiveness) and the stage at diagnosis or length of survival can be explained by differential biomarker expression between different subtypes of ovarian carcinoma. They also address the question of whether early stage and late stage ovarian carcinomas are fundamentally different.
What Did the Researchers Do and Find?
The researchers measured the expression of 21 candidate protein biomarkers in 500 ovarian carcinoma samples collected in British Columbia, Canada, between 1984 and 2000. For 20 of the biomarkers, the fraction of tumors expressing the biomarker varied significantly between ovarian carcinoma subtypes. Considering all the tumors together, ten biomarkers had different expression levels in early and late stage tumors. However, when each subtype was considered separately, the expression of none of the biomarkers varied with stage. When the researchers asked whether the expression of any of the biomarkers correlated with survival times, they found that nine biomarkers were unfavorable indicators of outcome when all the tumors were considered together. That is, women whose tumors expressed any of these biomarkers had a higher risk of dying from ovarian cancer than women whose tumors did not express these biomarkers. However, only three biomarkers were unfavorable indicators for high-grade serous carcinomas considered alone and the expression of a biomarker called WT1 in this subtype of ovarian carcinoma is associated with a lower risk of dying. Similarly, expression of the biomarker Ki-67 was an unfavorable prognostic indicator when all the tumors were considered, but was not a prognostic indicator for any individual subtype.
What Do These Findings Mean?
These and other findings indicate that biomarker expression is more strongly associated with ovarian carcinoma subtype than with stage. In other words, biomarker expression is constant from early to late stage, but only within a given subtype. Second, the association of biomarker expression with survival varies between subtypes, hence lumping all subtypes together can yield misleading results. Although these findings need confirming in more tumor samples, they support the view that ovarian carcinoma subtypes are different diseases. In practical terms, therefore, these findings suggest that better ways to detect and treat ovarian cancer are more likely to be found if future biomarker studies and clinical research studies investigate each subtype of ovarian carcinoma separately rather than grouping them all together.
Additional Information.
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides a brief description of what cancer is and how it develops and information on all aspects of ovarian cancer for patients and professionals. It also provides a fact sheet on tumor markers (in English and Spanish)
The UK charity Cancerbackup provides general information about cancer and more specific information about ovarian cancer, including tumor staging
PMCID: PMC2592352  PMID: 19053170
5.  Redox Imaging of Human Breast Cancer Core Biopsies 
Academic radiology  2013;20(6):764-768.
Rationale and Objectives
The clinical gold standard for breast cancer diagnosis relies on invasive biopsies followed by tissue fixation for subsequent histopathological examination. This process renders the specimens to be much less suitable for biochemical or metabolic analysis. Our previous metabolic imaging data in tumor xenograft models showed that the mitochondrial redox state is a sensitive indicator that can distinguish between normal and tumor tissue. In this study, we investigated whether the same redox imaging technique can be applied to core biopsy samples of human breast cancer and whether the mitochondrial redox state may serve as a novel metabolic biomarker that may be used to distinguish between normal and malignant breast tissue in the clinic. Our long-term objective was to identify novel metabolic imaging biomarkers for breast cancer diagnosis.
Materials and Methods
Both normal and cancerous tissue specimens were collected from the cancer-bearing breasts of three patients shortly after surgical resection. Core biopsies and tissue blocks were obtained from tumor and normal adjacent breast tissue, respectively. All specimens were snap-frozen with liquid nitrogen, embedded in chilled mounting medium with flavin adenine dinucleotide and reduced nicotinamide adenine dinucleotide reference standards adjacently placed, and scanned using the Chance redox scanner (ie, cryogenic nicotinamide adenine dinucleotide/oxidized flavoprotein fluorescence imager).
Our preliminary data showed cancerous tissues had up to 10-fold higher oxidized flavoprotein signals and had elevated oxidized redox state compared to the normal tissues from the same patient. A high degree of tumor tissue heterogeneity in the redox indices was observed.
Our finding suggests that the identified redox imaging indices could differentiate between cancer and noncancer breast tissues without subjecting tissues to fixatives. We propose that this novel redox scanning procedure may assist in tissue diagnosis in freshly procured biopsy samples before tissue fixation.
PMCID: PMC3791620  PMID: 23664401
Mitochondria; oxidized flavoprotein (Fp); NADH; redox state; metabolic biomarker; the Chance redox scanner
6.  EBUS-TBNA Provides Highest RNA Yield for Multiple Biomarker Testing from Routinely Obtained Small Biopsies in Non-Small Cell Lung Cancer Patients - A Comparative Study of Three Different Minimal Invasive Sampling Methods 
PLoS ONE  2013;8(10):e77948.
Multiple biomarker testing is necessary to facilitate individualized treatment of lung cancer patients. More than 80% of lung cancers are diagnosed based on very small tumor samples. Often there is not enough tissue for molecular analysis. We compared three minimal invasive sampling methods with respect to RNA quantity for molecular testing.
106 small biopsies were prospectively collected by three different methods forceps biopsy, endobronchial ultrasound (EBUS) guided transbronchial needle aspiration (TBNA), and CT-guided core biopsy. Samples were split into two halves. One part was formalin fixed and paraffin embedded for standard pathological evaluation. The other part was put in RNAlater for immediate RNA/DNA extraction. If the pathologist confirmed the diagnosis of non-small cell lung cancer(NSCLC), the following molecular markers were tested: EGFR mutation, ERCC1, RRM1 and BRCA1.
Overall, RNA-extraction was possible in 101 out of 106 patients (95.3%). We found 49% adenocarcinomas, 38% squamouscarcinomas, and 14% non-otherwise-specified(NOS). The highest RNA yield came from endobronchial ultrasound guided needle aspiration, which was significantly higher than bronchoscopy (37.74±41.09 vs. 13.74±15.53 ng respectively, P = 0.005) and numerically higher than CT-core biopsy (37.74±41.09 vs. 28.72±44.27 ng respectively, P = 0.244). EGFR mutation testing was feasible in 100% of evaluable patients and its incidence was 40.8%, 7.9% and 14.3% in adenocarcinomas, squamouscarcinomas and NSCLC NOS subgroup respectively. There was no difference in the feasibility of molecular testing between the three sampling methods with feasibility rates for ERCC1, RRM1 and BRCA1 of 91%, 87% and 81% respectively.
All three methods can provide sufficient tumor material for multiple biomarkers testing from routinely obtained small biopsies in lung cancer patients. In our study EBUS guided needle aspiration provided the highest amount of tumor RNA compared to bronchoscopy or CT guided core biopsy. Thus EBUS should be considered as an acceptable option for tissue acquisition for molecular testing.
PMCID: PMC3812131  PMID: 24205040
7.  Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons 
PLoS Medicine  2014;11(2):e1001606.
In this study, Würtz and colleagues conducted high-throughput profiling of blood specimens in two large population-based cohorts in order to identify biomarkers for all-cause mortality and enhance risk prediction. The authors found that biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. However, further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers to guide screening and prevention.
Please see later in the article for the Editors' Summary
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
A biomarker is a biological molecule found in blood, body fluids, or tissues that may signal an abnormal process, a condition, or a disease. The level of a particular biomarker may indicate a patient's risk of disease, or likely response to a treatment. For example, cholesterol levels are measured to assess the risk of heart disease. Most current biomarkers are used to test an individual's risk of developing a specific condition. There are none that accurately assess whether a person is at risk of ill health generally, or likely to die soon from a disease. Early and accurate identification of people who appear healthy but in fact have an underlying serious illness would provide valuable opportunities for preventative treatment.
While most tests measure the levels of a specific biomarker, there are some technologies that allow blood samples to be screened for a wide range of biomarkers. These include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. These tools have the potential to be used to screen the general population for a range of different biomarkers.
Why Was This Study Done?
Identifying new biomarkers that provide insight into the risk of death from all causes could be an important step in linking different diseases and assessing patient risk. The authors in this study screened patient samples using NMR spectroscopy for biomarkers that accurately predict the risk of death particularly amongst the general population, rather than amongst people already known to be ill.
What Did the Researchers Do and Find?
The researchers studied two large groups of people, one in Estonia and one in Finland. Both countries have set up health registries that collect and store blood samples and health records over many years. The registries include large numbers of people who are representative of the wider population.
The researchers first tested blood samples from a representative subset of the Estonian group, testing 9,842 samples in total. They looked at 106 different biomarkers in each sample using NMR spectroscopy. They also looked at the health records of this group and found that 508 people died during the follow-up period after the blood sample was taken, the majority from heart disease, cancer, and other diseases. Using statistical analysis, they looked for any links between the levels of different biomarkers in the blood and people's short-term risk of dying. They found that the levels of four biomarkers—plasma albumin, alpha-1-acid glycoprotein, very-low-density lipoprotein (VLDL) particle size, and citrate—appeared to accurately predict short-term risk of death. They repeated this study with the Finnish group, this time with 7,503 individuals (176 of whom died during the five-year follow-up period after giving a blood sample) and found similar results.
The researchers carried out further statistical analyses to take into account other known factors that might have contributed to the risk of life-threatening illness. These included factors such as age, weight, tobacco and alcohol use, cholesterol levels, and pre-existing illness, such as diabetes and cancer. The association between the four biomarkers and short-term risk of death remained the same even when controlling for these other factors.
The analysis also showed that combining the test results for all four biomarkers, to produce a biomarker score, provided a more accurate measure of risk than any of the biomarkers individually. This biomarker score also proved to be the strongest predictor of short-term risk of dying in the Estonian group. Individuals with a biomarker score in the top 20% had a risk of dying within five years that was 19 times greater than that of individuals with a score in the bottom 20% (288 versus 15 deaths).
What Do These Findings Mean?
This study suggests that there are four biomarkers in the blood—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—that can be measured by NMR spectroscopy to assess whether otherwise healthy people are at short-term risk of dying from heart disease, cancer, and other illnesses. However, further validation of these findings is still required, and additional studies should examine the biomarker specificity and associations in settings closer to clinical practice. The combined biomarker score appears to be a more accurate predictor of risk than tests for more commonly known risk factors. Identifying individuals who are at high risk using these biomarkers might help to target preventative medical treatments to those with the greatest need.
However, there are several limitations to this study. As an observational study, it provides evidence of only a correlation between a biomarker score and ill health. It does not identify any underlying causes. Other factors, not detectable by NMR spectroscopy, might be the true cause of serious health problems and would provide a more accurate assessment of risk. Nor does this study identify what kinds of treatment might prove successful in reducing the risks. Therefore, more research is needed to determine whether testing for these biomarkers would provide any clinical benefit.
There were also some technical limitations to the study. NMR spectroscopy does not detect as many biomarkers as mass spectrometry, which might therefore identify further biomarkers for a more accurate risk assessment. In addition, because both study groups were northern European, it is not yet known whether the results would be the same in other ethnic groups or populations with different lifestyles.
In spite of these limitations, the fact that the same four biomarkers are associated with a short-term risk of death from a variety of diseases does suggest that similar underlying mechanisms are taking place. This observation points to some potentially valuable areas of research to understand precisely what's contributing to the increased risk.
Additional Information
Please access these websites via the online version of this summary at
The US National Institute of Environmental Health Sciences has information on biomarkers
The US Food and Drug Administration has a Biomarker Qualification Program to help researchers in identifying and evaluating new biomarkers
Further information on the Estonian Biobank is available
The Computational Medicine Research Team of the University of Oulu and the University of Bristol have a webpage that provides further information on high-throughput biomarker profiling by NMR spectroscopy
PMCID: PMC3934819  PMID: 24586121
8.  HER2 and ESR1 mRNA expression levels and response to neoadjuvant trastuzumab plus chemotherapy in patients with primary breast cancer 
Recent data suggest that benefit from trastuzumab and chemotherapy might be related to expression of HER2 and estrogen receptor (ESR1). Therefore, we investigated HER2 and ESR1 mRNA levels in core biopsies of HER2-positive breast carcinomas from patients treated within the neoadjuvant GeparQuattro trial.
HER2 levels were centrally analyzed by immunohistochemistry (IHC), silver in situ hybridization (SISH) and qRT-PCR in 217 pretherapeutic formalin-fixed, paraffin-embedded (FFPE) core biopsies. All tumors had been HER2-positive by local pathology and had been treated with neoadjuvant trastuzumab/ chemotherapy in GeparQuattro.
Only 73% of the tumors (158 of 217) were centrally HER2-positive (cHER2-positive) by IHC/SISH, with cHER2-positive tumors showing a significantly higher pCR rate (46.8% vs. 20.3%, P <0.0005). HER2 status by qRT-PCR showed a concordance of 88.5% with the central IHC/SISH status, with a low pCR rate in those tumors that were HER2-negative by mRNA analysis (21.1% vs. 49.6%, P <0.0005). The level of HER2 mRNA expression was linked to response rate in ESR1-positive tumors, but not in ESR1-negative tumors. HER2 mRNA expression was significantly associated with pCR in the HER2-positive/ESR1-positive tumors (P = 0.004), but not in HER2-positive/ESR1-negative tumors.
Only patients with cHER2-positive tumors - irrespective of the method used - have an increased pCR rate with trastuzumab plus chemotherapy. In patients with cHER2-negative tumors the pCR rate is comparable to the pCR rate in the non-trastuzumab treated HER-negative population. Response to trastuzumab is correlated to HER2 mRNA levels only in ESR1-positive tumors. This study adds further evidence to the different biology of both subsets within the HER2-positive group.
Introduction The human epidermal growth factor receptor 2 (HER2) is the prototype of a predictive biomarker for targeted treatment [1-8]. International initiatives have established the combination of immunohistochemistry (IHC) and in situ hybridization as the current gold standard [9,10]. As an additional approach determination of HER2 mRNA expression is technically feasible in formalin-fixed paraffin-embedded (FFPE) tissue [11-13]. Crosstalk between the estrogen receptor (ER) and the HER2 pathway has been suggested based on cell culture and animal models [14]. Consequently, the 2011 St Gallen panel has pointed out that HER2-positive tumors should be divided into two groups based on expression of the ER [15].
A retrospective analysis of the National Surgical Adjuvant Breast and Bowel Project (NSABP) B31 study has suggested that mRNA levels of HER2 and ESR1 might be relevant for the degree of benefit from adjuvant trastuzumab. By subpopulation treatment effect pattern plot (STEPP) analysis in ER-positive tumors, benefit from trastuzumab was shown to be restricted to those with higher levels of HER2 mRNA (S Paik, personal communication, results summarized in [15]).
In our study we evaluated this hypothesis in the neoadjuvant setting in a cohort of 217 patients from the neoadjuvant GeparQuattro trial [5]. All patients had been HER2- positive by local pathology assessment and had received 24 to 36 weeks of neoadjuvant trastuzumab plus an anthracycline/taxane-based chemotherapy. For central evaluation we used three different methods, HER2 IHC, and HER2 silver in situ hybridization (SISH), as well as measurement of HER2 mRNA by quantitative real-time (qRT)-PCR [11].
The primary objective of this analysis was to investigate if pathological complete response (pCR) rate in HER2-positive breast cancer would depend on the level of HER2 mRNA expression, with a separate analysis for HR-positive and -negative tumors. Central evaluation of the HER2 status showed that 27% of the tumors with HER2 overexpression by local pathology were HER2-negative. This enabled us to compare response rates in patients with HER2-positive and -negative tumors as a secondary objective.
PMCID: PMC3672694  PMID: 23391338
9.  Cancer Screening: A Mathematical Model Relating Secreted Blood Biomarker Levels to Tumor Sizes  
PLoS Medicine  2008;5(8):e170.
Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.
Methods and Findings
Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm3 and 3,610.14 mm3 for CA125 and between 0.21 mm3 and 131.51 mm3 for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm3 and 1.52 × 106 mm3 for CA125 and between 27 mm3 and 3.45 × 105 mm3 for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.
This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.
Sanjiv Gambhir and colleagues describe a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays.
Editors' Summary
Cancers—disorganized masses of cells that can occur in any tissue—develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer (tumor) is detected when it is small, surgery can often provide a cure. Unfortunately, many cancers (particularly those deep inside the body) are not detected until they are large enough to cause pain or other symptoms by pressing against surrounding tissue. By this time, it may be impossible to remove the original tumor surgically and there may be metastases scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. Consequently, researchers are trying to develop early detection tests for different types of cancer. Many tumors release specific proteins—“cancer biomarkers”—into the blood and the hope is that it might be possible to find sets of blood biomarkers that detect cancers when they are still small and thus save many lives.
Why Was This Study Done?
For most biomarkers, it is not known how the amount of protein detected in the blood relates to tumor size or how sensitive the assays for biomarkers must be to improve patient survival. In this study, the researchers develop a “linear one-compartment” mathematical model to predict how large tumors need to be before blood biomarkers can be used to detect them and test this model using published data on two established cancer biomarkers—CA125 and prostate-specific antigen (PSA). CA125 is used to monitor the progress of patients with ovarian cancer after treatment; ovarian cancer is rarely diagnosed in its early stages and only one-fourth of women with advanced disease survive for 5 y after diagnosis. PSA is used to screen for prostate cancer and has increased the detection of this cancer in its early stages when it is curable.
What Did the Researchers Do and Find?
To develop a model that relates secreted blood biomarker levels to tumor sizes, the researchers assumed that biomarkers mix evenly throughout the patient's blood, that cancer cells secrete biomarkers into the fluid that surrounds them, that 0.1%–20% of these secreted proteins enter the blood at a continuous rate, and that biomarkers are continuously removed from the blood. The researchers then used their model to calculate the smallest tumor sizes that might be detectable with these biomarkers by feeding in existing data on CA125 and on PSA, including assay detection limits and the biomarker secretion rates of cancer cells growing in dishes. When only tumor cells secreted the biomarker and 10% of the secreted biomarker reach the blood, the model predicted that ovarian tumors between 0.11 mm3 (smaller than a grain of salt) and nearly 4,000 mm3 (about the size of a cherry) would be detectable by measuring CA125 blood levels (the range was determined by varying the amount of biomarker secreted by the tumor cells and the assay sensitivity); for prostate cancer, the detectable tumor sizes ranged from similar lower size to about 130 mm3 (pea-sized). However, healthy cells often also secrete small quantities of cancer biomarkers. With this condition incorporated into the model, the estimated detectable tumor sizes (or total tumor burden including metastases) ranged between grape-sized and melon-sized for ovarian cancers and between pea-sized to about grapefruit-sized for prostate cancers.
What Do These Findings Mean?
The accuracy of the calculated tumor sizes provided by the researchers' mathematical model is limited by the lack of data on how tumors behave in the human body and by the many assumptions incorporated into the model. Nevertheless, the model predicts detection limits for ovarian and prostate cancer that broadly mirror the clinical performance of both biomarkers. Somewhat worryingly, the model also indicates that a tumor may have to be very large for blood biomarkers to reveal its presence, a result that could limit the clinical usefulness of biomarkers, especially if they are secreted not only by tumor cells but also by healthy cells. Given this finding, as more information about how biomarkers behave in the human body becomes available, this model (and more complex versions of it) should help researchers decide which biomarkers are likely to improve early cancer detection and patient outcomes.
Additional Information.
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides a brief description of what cancer is and how it develops and a fact sheet on tumor markers; it also provides information on all aspects of ovarian and prostate cancer for patients and professionals, including information on screening and testing (in English and Spanish)
The UK charity Cancerbackup also provides general information about cancer and more specific information about ovarian and prostate cancer, including the use of CA125 and PSA for screening and follow-up
The American Society of Clinical Oncology offers a wide range of information on various cancer types, including online published articles on the current status of cancer diagnosis and management from the educational book developed by the annual meeting faculty and presenters. Registration is mandatory, but information is free
PMCID: PMC2517618  PMID: 18715113
10.  Antimicrobial activity of UMFix tissue fixative 
Journal of Clinical Pathology  2005;58(1):22-25.
Aims: The aim of this study was to determine the antimicrobial effects of UMFix, an alcohol based tissue fixative, on various microorganisms. The UMFix solution was compared with 10% neutral buffered formalin.
Methods: Standard methods to determine microorganism colony counts were performed after exposure of the microorganisms to UMFix and 10% neutral buffered formalin.
Results: After a short exposure, UMFix rapidly killed vegetative bacteria, yeasts, moulds, and viruses. Bacterial spores were resistant to killing by UMFix. All organisms were killed by the 10% neutral buffered formalin preparation.
Conclusions: UMFix was microbicidal for vegetative bacteria, yeasts, and aspergillus species after a short exposure, although it was not active against spore forming bacillus species. The methanol content of the fixative was responsible for the killing effect of this fixative. No killing was seen when polyethylene glycol was used alone.
PMCID: PMC1770532  PMID: 15623477
UMFix; methanol; tissue fixation
11.  Detection of Squamous Cell Carcinoma and corresponding biomarkers using Optical Spectroscopy 
1) Investigate the use of optical reflectance spectroscopy to differentiate malignant and non-malignant tissues in head and neck lesions; 2) Characterize corresponding oxygen tissue biomarkers that are associated with pathologic diagnosis
Study Design
Prospective non-randomized clinical study
Tertiary VA Medical Center
Subjects and Methods
All patients undergoing panendoscopy with biopsy for suspected head and neck cancer were eligible. Prior to taking tissue samples, the optical probe was placed at three locations to collect diffuse reflectance data. These locations were labeled “tumor”, “immediately adjacent”, and “distant normal tissue”. Biopsies were taken of each of these respective sites. The diffuse reflectance spectra were analyzed, and biomarker specific absorption data was extracted using an inverse Monte Carlo algorithm for malignant and non-malignant tissues. Histopathological analysis was performed and used as the gold standard to analyze the optical biomarker data.
21 patients with mucosal squamous cell carcinoma of the head and neck were identified and selected to participate in the study. Statistically significant differences in oxygen saturation (p = 0.004) and oxygenated hemoglobin (p = 0.02) were identified between malignant and non-malignant tissues.
Our study established proof of principle that optical spectroscopy can be used in the head and neck areas to detect malignant tissue. Furthermore, tissue biomarkers were correlated with a diagnosis of malignancy.
PMCID: PMC3098757  PMID: 21493201
12.  Nuclear Receptor Expression Defines a Set of Prognostic Biomarkers for Lung Cancer 
PLoS Medicine  2010;7(12):e1000378.
David Mangelsdorf and colleagues show that nuclear receptor expression is strongly associated with clinical outcomes of lung cancer patients, and this expression profile is a potential prognostic signature for lung cancer patient survival time, particularly for individuals with early stage disease.
The identification of prognostic tumor biomarkers that also would have potential as therapeutic targets, particularly in patients with early stage disease, has been a long sought-after goal in the management and treatment of lung cancer. The nuclear receptor (NR) superfamily, which is composed of 48 transcription factors that govern complex physiologic and pathophysiologic processes, could represent a unique subset of these biomarkers. In fact, many members of this family are the targets of already identified selective receptor modulators, providing a direct link between individual tumor NR quantitation and selection of therapy. The goal of this study, which begins this overall strategy, was to investigate the association between mRNA expression of the NR superfamily and the clinical outcome for patients with lung cancer, and to test whether a tumor NR gene signature provided useful information (over available clinical data) for patients with lung cancer.
Methods and Findings
Using quantitative real-time PCR to study NR expression in 30 microdissected non-small-cell lung cancers (NSCLCs) and their pair-matched normal lung epithelium, we found great variability in NR expression among patients' tumor and non-involved lung epithelium, found a strong association between NR expression and clinical outcome, and identified an NR gene signature from both normal and tumor tissues that predicted patient survival time and disease recurrence. The NR signature derived from the initial 30 NSCLC samples was validated in two independent microarray datasets derived from 442 and 117 resected lung adenocarcinomas. The NR gene signature was also validated in 130 squamous cell carcinomas. The prognostic signature in tumors could be distilled to expression of two NRs, short heterodimer partner and progesterone receptor, as single gene predictors of NSCLC patient survival time, including for patients with stage I disease. Of equal interest, the studies of microdissected histologically normal epithelium and matched tumors identified expression in normal (but not tumor) epithelium of NGFIB3 and mineralocorticoid receptor as single gene predictors of good prognosis.
NR expression is strongly associated with clinical outcomes for patients with lung cancer, and this expression profile provides a unique prognostic signature for lung cancer patient survival time, particularly for those with early stage disease. This study highlights the potential use of NRs as a rational set of therapeutically tractable genes as theragnostic biomarkers, and specifically identifies short heterodimer partner and progesterone receptor in tumors, and NGFIB3 and MR in non-neoplastic lung epithelium, for future detailed translational study in lung cancer.
Please see later in the article for the Editors' Summary
Editors' Summary
Lung cancer, the most common cause of cancer-related death, kills 1.3 million people annually. Most lung cancers are “non-small-cell lung cancers” (NSCLCs), and most are caused by smoking. Exposure to chemicals in smoke causes changes in the genes of the cells lining the lungs that allow the cells to grow uncontrollably and to move around the body. How NSCLC is treated and responds to treatment depends on its “stage.” Stage I tumors, which are small and confined to the lung, are removed surgically, although chemotherapy is also sometimes given. Stage II tumors have spread to nearby lymph nodes and are treated with surgery and chemotherapy, as are some stage III tumors. However, because cancer cells in stage III tumors can be present throughout the chest, surgery is not always possible. For such cases, and for stage IV NSCLC, where the tumor has spread around the body, patients are treated with chemotherapy alone. About 70% of patients with stage I and II NSCLC but only 2% of patients with stage IV NSCLC survive for five years after diagnosis; more than 50% of patients have stage IV NSCLC at diagnosis.
Why Was This Study Done?
Patient responses to treatment vary considerably. Oncologists (doctors who treat cancer) would like to know which patients have a good prognosis (are likely to do well) to help them individualize their treatment. Consequently, the search is on for “prognostic tumor biomarkers,” molecules made by cancer cells that can be used to predict likely clinical outcomes. Such biomarkers, which may also be potential therapeutic targets, can be identified by analyzing the overall pattern of gene expression in a panel of tumors using a technique called microarray analysis and looking for associations between the expression of sets of genes and clinical outcomes. In this study, the researchers take a more directed approach to identifying prognostic biomarkers by investigating the association between the expression of the genes encoding nuclear receptors (NRs) and clinical outcome in patients with lung cancer. The NR superfamily contains 48 transcription factors (proteins that control the expression of other genes) that respond to several hormones and to diet-derived fats. NRs control many biological processes and are targets for several successful drugs, including some used to treat cancer.
What Did the Researchers Do and Find?
The researchers analyzed the expression of NR mRNAs using “quantitative real-time PCR” in 30 microdissected NSCLCs and in matched normal lung tissue samples (mRNA is the blueprint for protein production). They then used an approach called standard classification and regression tree analysis to build a prognostic model for NSCLC based on the expression data. This model predicted both survival time and disease recurrence among the patients from whom the tumors had been taken. The researchers validated their prognostic model in two large independent lung adenocarcinoma microarray datasets and in a squamous cell carcinoma dataset (adenocarcinomas and squamous cell carcinomas are two major NSCLC subtypes). Finally, they explored the roles of specific NRs in the prediction model. This analysis revealed that the ability of the NR signature in tumors to predict outcomes was mainly due to the expression of two NRs—the short heterodimer partner (SHP) and the progesterone receptor (PR). Expression of either gene could be used as a single gene predictor of the survival time of patients, including those with stage I disease. Similarly, the expression of either nerve growth factor induced gene B3 (NGFIB3) or mineralocorticoid receptor (MR) in normal tissue was a single gene predictor of a good prognosis.
What Do These Findings Mean?
These findings indicate that the expression of NR mRNA is strongly associated with clinical outcomes in patients with NSCLC. Furthermore, they identify a prognostic NR expression signature that provides information on the survival time of patients, including those with early stage disease. The signature needs to be confirmed in more patients before it can be used clinically, and researchers would like to establish whether changes in mRNA expression are reflected in changes in protein expression if NRs are to be targeted therapeutically. Nevertheless, these findings highlight the potential use of NRs as prognostic tumor biomarkers. Furthermore, they identify SHP and PR in tumors and two NRs in normal lung tissue as molecules that might provide new targets for the treatment of lung cancer and new insights into the early diagnosis, pathogenesis, and chemoprevention of lung cancer.
Additional Information
Please access these Web sites via the online version of this summary at
The Nuclear Receptor Signaling Atlas (NURSA) is consortium of scientists sponsored by the US National Institutes of Health that provides scientific reagents, datasets, and educational material on nuclear receptors and their co-regulators to the scientific community through a Web-based portal
The Cancer Prevention and Research Institute of Texas (CPRIT) provides information and resources to anyone interested in the prevention and treatment of lung and other cancers
The US National Cancer Institute provides detailed information for patients and professionals about all aspects of lung cancer, including information on non-small-cell carcinoma and on tumor markers (in English and Spanish)
Cancer Research UK also provides information about lung cancer and information on how cancer starts
MedlinePlus has links to other resources about lung cancer (in English and Spanish)
Wikipedia has a page on nuclear receptors (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
PMCID: PMC3001894  PMID: 21179495
13.  P24-T Quantitative Proteomics of Formalin-Fixed Archival Tissue 
Capabilities for quantitative mass spectrometry–based proteomic profiling of formalin-fixed archival tissue have been developed. Using Liquid Tissue reagents and protocols, we have effectively profiled and validated known and novel cancer biomarkers across a wide variety of fixed cancer tissue samples. Recently developed applications include quantification of protein expression in fixed archival tissue with an emphasis on oral cavity (head and neck squamous cell carcinoma—HNSCC) and breast cancer. Spectral count bioinformatics provides a non-labeling method for quantitative proteomics of formalin-fixed histologically-defined oral cavity cancer. Results indicate many proteins were differentially expressed in cells obtained by laser microdissection of normal, highly differentiated, moderately differentiated, and poorly differentiated HNSCC fixed tissue. Candidate protein biomarkers found to be differentially expressed in the process of HNSCC progression were confirmed and validated by immunohistochemistry on large panels of HNSCC tissue. In addition, we have developed and evaluated a method for direct detection and absolute quantification by selected reaction monitoring (SRM) of HER2 directly in formalin-fixed paraffin-embedded breast cancer tissue using a stable isotope standard peptide derived from HER2. Soluble protein extracts from a collection of breast cancer tissues known to express a range of HER2 were prepared using Liquid Tissue reagents, and quantitative levels were determined. Results demonstrate the ability to quantitate HER2 expression in Liquid Tissue extracts from fixed tissue sections that correlate with standard IHC and indicate the ability to quantify HER2 in immunohistochemical-negative cells. These cumulative results demonstrate development of technologies for quantitative proteomic analysis of proteins that can be applied to the vast worldwide formalin-fixed tissue archives.
PMCID: PMC2292067
14.  Heterogeneity of amplification of HER2, EGFR, CCND1 and MYC in gastric cancer 
BMC Gastroenterology  2015;15:7.
Intra-tumor heterogeneity is a potential cause for failure of targeted therapy in gastric cancer, but the extent of heterogeneity of established (HER2) or potential (EGFR, CCND1) target genes and prognostic gene alterations (MYC) had not been systematically studied.
To study heterogeneity of these genes in a large patient cohort, a heterogeneity tissue microarray was constructed containing 0.6 mm tissue cores from 9 different areas of the primary gastric cancers of 113 patients and matched lymph node metastases from 61 of these patients. Dual color fluorescence in-situ hybridization was performed to assess amplification of HER2, EGFR, CCND1 and MYC using established thresholds (ratio ≥ 2.0). Her2 immunohistochemistry (IHC) was performed in addition.
Amplification was found in 17.4% of 109 interpretable cases for HER2, 6.4% for EGFR, 17.4% for CCND1, and 24.8% for MYC. HER2 amplification was strongly linked to protein overexpression by IHC in a spot-by-spot analysis (p < 0.0001). Intra-tumor heterogeneity was found in the primary tumors of 9 of 19 (47.3%) cancers with HER2, 8 of 17 (47.0%) cancers with CCND1, 5 of 7 (71.4%) cancers with EGFR, and 23 of 27 (85.2%) cancers with MYC amplification. Amplification heterogeneity was particularly frequent in case of low-level amplification (<10 gene copies). While the amplification status was often different between metastases, unequivocal intra-tumor heterogeneity was not found in individual metastases.
The data of our study demonstrate that heterogeneity is common for biomarkers in gastric cancer. Given that both TMA tissue cores and clinical tumor biopsies analyze only a small fraction of the tumor bulk, it can be concluded that such heterogeneity may potentially limit treatment decisions based on the analysis of a single clinical cancer biopsy.
Electronic supplementary material
The online version of this article (doi:10.1186/s12876-015-0231-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4324419  PMID: 25649416
HER2; EGFR; MYC; CCND1; Gene amplification; Gastric cancer; Heterogeneity tissue microarray
15.  Liquid Chromatography-Tandem and MALDI imaging mass spectrometry analyses of RCL2/CS100-fixed paraffin embedded tissues: proteomics evaluation of an alternate fixative for biomarker discovery 
Journal of proteome research  2009;8(12):5619-5628.
Human tissues are an important source of biological material for the discovery of novel biomarkers. Fresh-frozen tissue could represent an ideal supply of archival material for molecular investigations. However, immediate flash freezing is usually not possible, especially for rare or valuable tissue samples such as biopsies. Here, we investigated the compatibility of RCL2/CS100, a non-crosslinking, non-toxic, and non-volatile organic fixative, with shotgun proteomic analyses. Several protein extraction protocols compatible with mass spectrometry were investigated from RCL2/CS100-fixed and fresh-frozen colonic mucosa, breast, and prostate tissues. The peptides and proteins identified from RCL2/CS100 tissue were then comprehensively compared with those identified from matched fresh-frozen tissues using a bottom-up strategy based on nano-reversed phase liquid chromatography coupled with tandem mass spectrometry (nanoRPLC-MS/MS). Results showed that similar peptides could be identified in both archival conditions and the proteome coverage was not obviously compromised by the RCL2/CS100 fixation process. NanoRPLC-MS/MS of laser capture microdissected RCL2/CS100-fixed tissues gave the same amount of biological information as that recovered from whole RCL2/CS100-fixed or frozen tissues. We next performed MALDI tissue profiling and imaging mass spectrometry and observed a high level of agreement in protein expression as well as excellent agreement between the images obtained from RCL2/CS100-fixed and fresh-frozen tissue samples. These results suggest that RCL2/CS100-fixed tissues are suitable for shotgun proteomic analyses and tissue imaging. More importantly, this alternate fixative opens the door to the analysis of small, valuable, and rare target lesions that are usually inaccessible to complementary biomarker-driven genomic and proteomic research.
PMCID: PMC2924679  PMID: 19856998
16.  Comparative analysis of volatile metabolomics signals from melanoma and benign skin: a pilot study 
Metabolomics  2013;9(5):998-1008.
The analysis of volatile organic compounds (VOC) as biomarkers of cancer is both promising and challenging. In this pilot study, we used an untargeted approach to compare volatile metabolomic signatures of melanoma and matched control non-neoplastic skin from the same patient. VOC from fresh (non-fixed) biopsied tissue were collected using the headspace solid phase micro extraction method (HS SPME) and analyzed by gas chromatography and mass spectrometry (GCMS). We applied the XCMS analysis platform and MetaboAnalyst software to reveal many differentially expressed metabolic features. Our analysis revealed increased levels of lauric acid (C12:0) and palmitic acid (C16:0) in melanoma. The identity of these compounds was confirmed by comparison with chemical standards. Increased levels of these fatty acids are likely to be a consequence of up-regulated de novo lipid synthesis, a known characteristic of cancer. Increased oxidative stress is likely to cause an additional increase in lauric acid. Implementation of this study design on larger number of cases will be necessary for the future metabolomics biomarker discovery applications.
Electronic supplementary material
The online version of this article (doi:10.1007/s11306-013-0523-z) contains supplementary material, which is available to authorized users.
PMCID: PMC3769583  PMID: 24039618
Volatile organic compounds; Skin cancer; Metabolites; GCMS; Palmitic acid
17.  Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis 
Human pathology  2009;40(12):1671-1678.
The differential diagnosis of clear cell, papillary and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative RT-PCR can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary and 10 chromophobe renal cell carcinoma; the microarray data was derived from two independent published studies. The ArrayWiki biocomputing system merged the microarray datasets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms, in order to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected six novel candidate biomakers from the omniBioMarker analysis, and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative RT-PCR and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1. Quantitative RT-PCR was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary and 12 chromophobe renal cell carcinoma. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.
PMCID: PMC2783948  PMID: 19695674
Renal Cell Carcinoma; Microarrays; Quantitative RT-PCR; Immunohistochemistry; Bioinformatics
18.  MicroRNA Analysis Using RNA Extracted from Matched Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen Samples on SOLiD™ System 
Archived formalin-fixed paraffin-embedded (FFPE) specimens represent excellent resources for biomarker discovery, but it has been a major challenge to study gene expression in these samples due to mRNA degradation and modification during fixation and processing. MicroRNAs (miRNAs) regulate gene expression at post-transcriptional level and are considered as important regulators of cancer progression. Next generation sequencing technologies such as SOLiD™ provide an ideal method for measuring the abundance of miRNA molecules in different cancer stages and provide insightful information on tumorigenesis. However, currently there is no good method to systematically study miRNA expression in FFPE samples on next generation sequencing platforms. We have designed and developed a ligation-based miRNA detection method to capture small RNA sequences in FFPE samples and convert them into templates suitable for sequencing on the SOLiD™ System. Total RNA was isolated from matched lung adenocarcinoma FFPE and snap frozen tissues using an Ambion RecoverAll™ kit. A PureLink™ miRNA Isolation kit was used to enrich the small RNA fraction in these total RNA samples. Library preparation using a SOLiD™ Total RNA-Seq kit with modified protocol was performed on the enriched RNA followed by sequencing on SOLiD™ system. Our results show that small RNA extracted from FFPE samples was successfully converted to small RNA libraries. Very similar mapping statistics were obtained from matched FFPE and fresh-frozen samples after SOLiD™ sequencing. A good correlation of miRNA expression pattern was also observed. This suggests that miRNA molecules are less affected by sample degradation and RNA-protein crosslink. This study provides a foundation for miRNA expression analysis on SOLiD™ system using FFPE samples in cancer and other diseases.
PMCID: PMC3186652
19.  Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood 
BMC Medical Genomics  2013;6(Suppl 1):S4.
Breast cancer is worldwide the second most common type of cancer after lung cancer. Traditional mammography and Tissue Microarray has been studied for early cancer detection and cancer prediction. However, there is a need for more reliable diagnostic tools for early detection of breast cancer. This can be a challenge due to a number of factors and logistics. First, obtaining tissue biopsies can be difficult. Second, mammography may not detect small tumors, and is often unsatisfactory for younger women who typically have dense breast tissue. Lastly, breast cancer is not a single homogeneous disease but consists of multiple disease states, each arising from a distinct molecular mechanism and having a distinct clinical progression path which makes the disease difficult to detect and predict in early stages.
In the paper, we present a Support Vector Machine based on Recursive Feature Elimination and Cross Validation (SVM-RFE-CV) algorithm for early detection of breast cancer in peripheral blood and show how to use SVM-RFE-CV to model the classification and prediction problem of early detection of breast cancer in peripheral blood.
The training set which consists of 32 health and 33 cancer samples and the testing set consisting of 31 health and 34 cancer samples were randomly separated from a dataset of peripheral blood of breast cancer that is downloaded from Gene Express Omnibus. First, we identified the 42 differentially expressed biomarkers between "normal" and "cancer". Then, with the SVM-RFE-CV we extracted 15 biomarkers that yield zero cross validation score. Lastly, we compared the classification and prediction performance of SVM-RFE-CV with that of SVM and SVM Recursive Feature Elimination (SVM-RFE).
We found that 1) the SVM-RFE-CV is suitable for analyzing noisy high-throughput microarray data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance (Area Under Curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the biomarkers are associated with Signaling, Hemostasis, Hormones, and Immune System, which are consistent with previous findings. Our prediction model can serve as a general model for biomarker discovery in early detection of other cancers. In the future, Polymerase Chain Reaction (PCR) is planned for validation of the ability of these potential biomarkers for early detection of breast cancer.
PMCID: PMC3552693  PMID: 23369435
20.  Effect of buffered formalin on amplification of DNA from paraffin wax embedded small biopsies using real-time PCR 
Journal of Clinical Pathology  2004;57(6):654-656.
Background: The isolation of good quality DNA from routinely fixed and processed biopsy samples is crucial for the success of subsequent molecular analysis.
Aims: To compare the amount of β actin DNA extracted from upper gastrointestinal tract biopsies fixed in buffered and unbuffered formalin.
Methods: Amounts of β actin DNA extracted from forceps biopsies of the upper gastrointestinal tract fixed in unbuffered (n  =  22) and buffered formalin (n  =  16) were estimated by quantitative real-time polymerase chain reaction.
Results: The yield of β actin DNA was significantly higher in biopsies fixed in buffered formalin than in those fixed in unbuffered formalin (median 2.8 × 104 and 5.3 × 102 DNA molecules, respectively; p < 0.005). Furthermore, fixation in buffered formalin led to a more reproducible DNA extraction, as indicated by the coefficient of variation (1.0 and 2.2, respectively).
Conclusions: This study indicates that tissue samples should be fixed in buffered formalin to facilitate the use of molecular pathology analysis in routine biopsy material.
PMCID: PMC1770336  PMID: 15166276
unbuffered formalin fixation; gastric and duodenal biopsy; paraffin wax embedding; real-time (TaqMan) polymerase chain reaction; human β actin
21.  Immunohistochemical assays in prostatic biopsies processed in Bouin’s fixative 
Journal of Clinical Pathology  2005;58(3):322-324.
Aims: To investigate the problems involved in undertaking immunohistochemistry (IHC) and nuclear morphometry using Bouin’s fixed prostate biopsies.
Methods: Archival Bouin’s fixed and formalin fixed, paraffin wax embedded prostatic biopsies were immunostained for three nuclear biomarkers (minichromosome maintenance protein 2 (MCM-2), p27, and Ki-67), one membrane localised biomarker (C-erb-B2), CD34, and α methylacyl-CoA racemase (AMACR). The quality of IHC staining was compared between tissues prepared separately in both fixatives. Feulgen staining was also performed on Bouin’s fixed tissues to check its suitability for nuclear morphometry.
Results: MCM-2 staining was completely negative in Bouin’s fixed tissues, whereas p27 showed more background and excess cytoplasmic staining in Bouin’s fixed versus formalin fixed tissues. C-erb-B2 showed non-specific, strong luminal cell staining in the Bouin’s fixed tissue. Feulgen staining was also very weak in Bouin’s fixed tissue. However, Ki-67, AMACR, and CD34 worked equally well in Bouin’s and formalin fixed tissues.
Conclusions: Bouin’s fixed tissues may be unsuitable when subsequent IHC and morphometry are contemplated. An awareness of which antibodies are suitable for use in Bouin’s fixed biopsies is essential.
PMCID: PMC1770607  PMID: 15735170
Bouin’s fixative; immunohistochemistry; prostate
22.  Developing a nanoparticle test for prostate cancer scoring 
Over-diagnosis and treatment of prostate cancer has been a major problem in prostate cancer care and management. Currently the most relevant prognostic factor to predict a patient's risk of death due to prostate cancer is the Gleason score of the biopsied tissue samples. However, pathological analysis is subjective, and the Gleason score is only a qualitative estimate of the cancer malignancy. Molecular biomarkers and diagnostic tests that can accurately predict prostate tumor aggressiveness are rather limited.
We report here for the first time the development of a nanoparticle test that not only can distinguish prostate cancer from normal and benign conditions, but also has the potential to predict the aggressiveness of prostate cancer quantitatively. To conduct the test, a prostate tissue lysate sample is spiked into a blood serum or human IgG solution and the spiked sample is incubated with a citrate-protected gold nanoparticle solution. IgG is known to adsorb to citrate-protected gold nanoparticles to form a "protein corona" on the nanoparticle surface. From this study, we discovered that certain tumor-specific molecules can interact with IgG and change the adsorption behavior of IgG to the gold nanoparticles. This change is reflected in the nanoparticle size of the assay solution and detected by a dynamic light scattering technique. Assay data were analyzed by one-way ANOVA for multiple variant analysis, and using the Student t-test or nonparametric Mann-Whitney U-tests for pairwise analyses.
An inverse, quantitative correlation of the average nanoparticle size of the assay solution with tumor status and histological diagnostic grading was observed from the nanoparticle test. IgG solutions spiked with prostate tumor tissue exhibit significantly smaller nanoparticle size than the solutions spiked with normal and benign tissues. The higher grade the tumor is, the smaller the nanoparticle size is. The test particularly revealed large differences among the intermediate Grade 2 tumors, and suggested the need to treat them differently.
Development of a new nanoparticle test may provide a quantitative measure of the prostate cancer aggressiveness. If validated in a larger study of patients with prostate cancer, this test could become a new diagnostic tool in conjunction with Gleason Score pathology diagnostics to better distinguish aggressive cancer from indolent tumor.
PMCID: PMC3337274  PMID: 22404986
Prostate cancer; Cancer aggressiveness; Biomarker; Nanoparticle; Molecular diagnostics
23.  Inverse correlation between PDGFC expression and lymphocyte infiltration in human papillary thyroid carcinomas 
BMC Cancer  2009;9:425.
Members of the PDGF family have been suggested as potential biomarkers for papillary thyroid carcinomas (PTC). However, it is known that both expression and stimulatory effect of PDGF ligands can be affected by inflammatory cytokines. We have performed a microarray study in a collection of PTCs, of which about half the biopsies contained tumour-infiltrating lymphocytes or thyroiditis. To investigate the expression level of PDGF ligands and receptors in PTC we measured the relative mRNA expression of all members of the PDGF family by qRT-PCR in 10 classical PTC, eight clinically aggressive PTC, and five non-neoplastic thyroid specimens, and integrated qRT-PCR data with microarray data to enable us to link PDGF-associated gene expression profiles into networks based on recognized interactions. Finally, we investigated potential influence on PDGF mRNA levels by the presence of tumour-infiltrating lymphocytes.
qRT-PCR was performed on PDGFA, PDGFB, PDGFC, PDGFD, PDGFRA PDGFRB and a selection of lymphocyte specific mRNA transcripts. Semiquantitative assessment of tumour-infiltrating lymphocytes was performed on the adjacent part of the biopsy used for RNA extraction for all biopsies, while direct quantitation by qRT-PCR of lymphocyte-specific mRNA transcripts were performed on RNA also subjected to expression analysis. Relative expression values of PDGF family members were combined with a cDNA microarray dataset and analyzed based on clinical findings and PDGF expression patterns. Ingenuity Pathway Analysis (IPA) was used to elucidate potential molecular interactions and networks.
PDGF family members were differentially regulated at the mRNA level in PTC as compared to normal thyroid specimens. Expression of PDGFA (p = 0.003), PDGFB (p = 0.01) and PDGFC (p = 0.006) were significantly up-regulated in PTCs compared to non-neoplastic thyroid tissue. In addition, expression of PDGFC was significantly up-regulated in classical PTCs as compared to clinically aggressive PTCs (p = 0.006), and PDGFRB were significantly up-regulated in clinically aggressive PTCs (p = 0.01) as compared to non-neoplastic tissue. Semiquantitative assessment of lymphocytes correlated well with quantitation of lymphocyte-specific gene expression. Further more, by combining TaqMan and microarray data we found a strong inverse correlation between PDGFC expression and the expression of lymphocyte specific mRNAs.
At the mRNA level, several members of the PDGF family are differentially expressed in PTCs as compared to normal thyroid tissue. Of these, only the PDGFC mRNA expression level initially seemed to distinguish classical PTCs from the more aggressive PTCs. However, further investigation showed that PDGFC expression level correlated inversely to the expression of several lymphocyte specific genes, and to the presence of lymphocytes in the biopsies. Thus, we find that PDGFC mRNA expression were down-regulated in biopsies containing infiltrated lymphocytes or thyroiditis. No other PDGF family member could be linked to lymphocyte specific gene expression in our collection of PTCs biopsies.
PMCID: PMC2797817  PMID: 19968886
24.  Kinomic Profiling of Electromagnetic Navigational Bronchoscopy Specimens: A New Approach for Personalized Medicine 
PLoS ONE  2014;9(12):e116388.
Researchers are currently seeking relevant lung cancer biomarkers in order to make informed decisions regarding therapeutic selection for patients in so-called “precision medicine.” However, there are challenges to obtaining adequate lung cancer tissue for molecular analyses. Furthermore, current molecular testing of tumors at the genomic or transcriptomic level are very indirect measures of biological response to a drug, particularly for small molecule inhibitors that target kinases. Kinase activity profiling is therefore theorized to be more reflective of in vivo biology than many current molecular analysis techniques. As a result, this study seeks to prove the feasibility of combining a novel minimally invasive biopsy technique that expands the number of lesions amenable for biopsy with subsequent ex vivo kinase activity analysis.
Eight patients with lung lesions of varying location and size were biopsied using the novel electromagnetic navigational bronchoscopy (ENB) technique. Basal kinase activity (kinomic) profiles and ex vivo interrogation of samples in combination with tyrosine kinase inhibitors erlotinib, crizotinib, and lapatinib were performed by PamStation 12 microarray analysis.
Kinomic profiling qualitatively identified patient specific kinase activity profiles as well as patient and drug specific changes in kinase activity profiles following exposure to inhibitor. Thus, the study has verified the feasibility of ENB as a method for obtaining tissue in adequate quantities for kinomic analysis and has demonstrated the possible use of this tissue acquisition and analysis technique as a method for future study of lung cancer biomarkers.
We demonstrate the feasibility of using ENB-derived biopsies to perform kinase activity assessment in lung cancer patients.
PMCID: PMC4280210  PMID: 25549342
25.  Genomic Predictors for Recurrence Patterns of Hepatocellular Carcinoma: Model Derivation and Validation 
PLoS Medicine  2014;11(12):e1001770.
In this study, Lee and colleagues develop a genomic predictor that can identify patients at high risk for late recurrence of hepatocellular carcinoma (HCC) and provided new biomarkers for risk stratification.
Typically observed at 2 y after surgical resection, late recurrence is a major challenge in the management of hepatocellular carcinoma (HCC). We aimed to develop a genomic predictor that can identify patients at high risk for late recurrence and assess its clinical implications.
Methods and Findings
Systematic analysis of gene expression data from human liver undergoing hepatic injury and regeneration revealed a 233-gene signature that was significantly associated with late recurrence of HCC. Using this signature, we developed a prognostic predictor that can identify patients at high risk of late recurrence, and tested and validated the robustness of the predictor in patients (n = 396) who underwent surgery between 1990 and 2011 at four centers (210 recurrences during a median of 3.7 y of follow-up). In multivariate analysis, this signature was the strongest risk factor for late recurrence (hazard ratio, 2.2; 95% confidence interval, 1.3–3.7; p = 0.002). In contrast, our previously developed tumor-derived 65-gene risk score was significantly associated with early recurrence (p = 0.005) but not with late recurrence (p = 0.7). In multivariate analysis, the 65-gene risk score was the strongest risk factor for very early recurrence (<1 y after surgical resection) (hazard ratio, 1.7; 95% confidence interval, 1.1–2.6; p = 0.01). The potential significance of STAT3 activation in late recurrence was predicted by gene network analysis and validated later. We also developed and validated 4- and 20-gene predictors from the full 233-gene predictor. The main limitation of the study is that most of the patients in our study were hepatitis B virus–positive. Further investigations are needed to test our prediction models in patients with different etiologies of HCC, such as hepatitis C virus.
Two independently developed predictors reflected well the differences between early and late recurrence of HCC at the molecular level and provided new biomarkers for risk stratification.
Please see later in the article for the Editors' Summary
Editors' Summary
Primary liver cancer—a tumor that starts when a liver cell acquires genetic changes that allow it to grow uncontrollably—is the second-leading cause of cancer-related deaths worldwide, killing more than 600,000 people annually. If hepatocellular cancer (HCC; the most common type of liver cancer) is diagnosed in its early stages, it can be treated by surgically removing part of the liver (resection), by liver transplantation, or by local ablation, which uses an electric current to destroy the cancer cells. Unfortunately, the symptoms of HCC, which include weight loss, tiredness, and jaundice (yellowing of the skin and eyes), are vague and rarely appear until the cancer has spread throughout the liver. Consequently, HCC is rarely diagnosed before the cancer is advanced and untreatable, and has a poor prognosis (likely outcome)—fewer than 5% of patients survive for five or more years after diagnosis. The exact cause of HCC is unclear, but chronic liver (hepatic) injury and inflammation (caused, for example, by infection with hepatitis B virus [HBV] or by alcohol abuse) promote tumor development.
Why Was This Study Done?
Even when it is diagnosed early, HCC has a poor prognosis because it often recurs. Patients treated for HCC can experience two distinct types of tumor recurrence. Early recurrence, which usually happens within the first two years after surgery, arises from the spread of primary cancer cells into the surrounding liver that left behind during surgery. Late recurrence, which typically happens more than two years after surgery, involves the development of completely new tumors and seems to be the result of chronic liver damage. Because early and late recurrence have different clinical courses, it would be useful to be able to predict which patients are at high risk of which type of recurrence. Given that injury, inflammation, and regeneration seem to prime the liver for HCC development, might the gene expression patterns associated with these conditions serve as predictive markers for the identification of patients at risk of late recurrence of HCC? Here, the researchers develop a genomic predictor for the late recurrence of HCC by examining gene expression patterns in tissue samples from livers that were undergoing injury and regeneration.
What Did the Researchers Do and Find?
By comparing gene expression data obtained from liver biopsies taken before and after liver transplantation or resection and recorded in the US National Center for Biotechnology Information Gene Expression Omnibus database, the researchers identified 233 genes whose expression in liver differed before and after liver injury (the hepatic injury and regeneration, or HIR, signature). Statistical analyses indicate that the expression of the HIR signature in archived tissue samples was significantly associated with late recurrence of HCC in three independent groups of patients, but not with early recurrence (a significant association between two variables is one that is unlikely to have arisen by chance). By contrast, a tumor-derived 65-gene signature previously developed by the researchers was significantly associated with early recurrence but not with late recurrence. Notably, as few as four genes from the HIR signature were sufficient to construct a reliable predictor for late recurrence of HCC. Finally, the researchers report that many of the genes in the HIR signature encode proteins involved in inflammation and cell death, but that others encode proteins involved in cellular growth and proliferation such as STAT3, a protein with a well-known role in liver regeneration.
What Do These Findings Mean?
These findings identify a gene expression signature that was significantly associated with late recurrence of HCC in three independent groups of patients. Because most of these patients were infected with HBV, the ability of the HIR signature to predict late occurrence of HCC may be limited to HBV-related HCC and may not be generalizable to HCC related to other causes. Moreover, the predictive ability of the HIR signature needs to be tested in a prospective study in which samples are taken and analyzed at baseline and patients are followed to see whether their HCC recurs; the current retrospective study analyzed stored tissue samples. Importantly, however, the HIR signature associated with late recurrence and the 65-gene signature associated with early recurrence provide new insights into the biological differences between late and early recurrence of HCC at the molecular level. Knowing about these differences may lead to new treatments for HCC and may help clinicians choose the most appropriate treatments for their patients.
Additional Information
Please access these websites via the online version of this summary at
The US National Cancer Institute provides information about all aspects of cancer, including detailed information for patients and professionals about primary liver cancer (in English and Spanish)
The American Cancer Society also provides information about liver cancer (including information on support programs and services; available in several languages)
The UK National Health Service Choices website provides information about primary liver cancer (including a video about coping with cancer)
Cancer Research UK (a not-for-profit organization) also provides detailed information about primary liver cancer (including information about living with primary liver cancer)
MD Anderson Cancer Center provides information about symptoms, diagnosis, treatment, and prevention of primary liver cancer
MedlinePlus provides links to further resources about liver cancer (in English and Spanish)
PMCID: PMC4275163  PMID: 25536056

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